The modality and pose variance between RGB and infrared (IR) images are two key challenges for RGB-IR person re-identification. Existing methods mainly focus on leveraging pixel or feature alignment to handle the intra-class variations and cross-modality discrepancy. However, these methods are hard to keep semantic identity consistency between global and local representation, which the consistency is important for the cross-modality pedestrian re-identification task. In this work, we propose a novel cross-modality graph reasoning method (CGRNet) to globally model and reason over relations between modalities and context, and to keep semantic identity consistency between global and local representation. Specifically, we propose a local modality-similarity module to put the distribution of modality-specific features into a common subspace without losing identity information. Besides, we squeeze the input feature of RGB and IR images into a channel-wise global vector, and through graph reasoning, the identity relationship and modality relationship in each vector are inferred. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-art. The code is available at https://github.com/fegnyujian/CGRNet.

中文翻译：

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RGB-红外行人重识别的高效跨模态图推理


RGB 和红外 (IR) 图像之间的模态和姿态差异是 RGB-IR 行人重新识别的两个关键挑战。现有的方法主要集中于利用像素或特征对齐来处理类内变化和跨模态差异。然而，这些方法很难保持全局和局部表示之间的语义身份一致性，而这种一致性对于跨模态行人重新识别任务很重要。在这项工作中，我们提出了一种新颖的跨模态图推理方法（CGRNet）来对模态和上下文之间的关系进行全局建模和推理，并保持全局和局部表示之间的语义一致性。具体来说，我们提出了一个局部模态相似性模块，将模态特定特征的分布放入公共子空间中，而不会丢失身份信息。此外，我们将RGB和IR图像的输入特征压缩到通道全局向量中，并通过图推理，推断每个向量中的恒等关系和模态关系。对两个数据集的广泛实验证明了我们的方法比现有最先进的方法具有优越的性能。代码可在 https://github.com/fegnyujian/CGRNet 获取。